Predictive modeling based on tumor spectral CT parameters and clinical features for postoperative complications in patients undergoing colon resection for cancer

Abstract

Background: Colon cancer is a particularly prevalent malignancy that produces postoperative complications (POCs). However, limited imaging modality exists on the accurate diagnosis of POCs. The purpose of this study was therefore to construct a model combining tumor spectral CT parameters and clinical features to predict POCs before surgery in colon cancer.

Methods: This retrospective study included 85 patients who had preoperative abdominal spectral CT scans and underwent radical colon cancer resection at our institution. The patients were divided into two groups based on the absence (no complication/grade I) or presence (grades II-V) of POCs according to the Clavien-Dindo grading system. The visceral fat areas (VFA) of patients were semi-automatically outlined and calculated on L3-level CT images using ImageJ software. Clinical features and tumor spectral CT parameters were statistically compared between the two groups. A combined model of spectral CT parameters and clinical features was established by stepwise regression to predict POCs in colon cancer. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve, including area under the curve (AUC), sensitivity, and specificity.

Results: Twenty-seven patients with POCs and 58 patients without POCs were included in this study. MonoE_40keV-VP and VFA were independent predictors of POCs. The combined model based on predictors yielded an AUC of 0.84 (95% CI: 0.74-0.91), with a sensitivity of 77.8% and specificity of 87.9%.

Conclusions: The model combining MonoE_40keV-VP and VFA can predict POCs before surgery in colon cancer and provide a basis for individualized management plans.

Critical relevance statement: The model combining MonoE40keV-VP and visceral fat area can predict postoperative complications before surgery in colon cancer and provide a basis for individualized management plans.

Key points: • Visceral fat area and MonoE40keV-VP were independent predictors of postoperative complications in colon cancer. • The combined model yielded a high AUC, sensitivity, and specificity in predicting postoperative complications. • The combined model was superior to the single visceral fat area or MonoE40keV-VP in predicting postoperative complications.

Keywords: Colon cancer; Combined model; Postoperative complications; Prediction; Spectral CT.